TRAINING SIGNAL DESIGN FOR CHANNEL ESTIMATION IN MASSIVE MIMO SYSTEMS

被引：0

作者：

Noh, Song ^{[1
]}

Zoltowski, Michael D. ^{[1
]}

Sung, Youngchul

Love, David J. ^{[1
]}

机构：

[1] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年

关键词：

Channel estimation; Kalman filtering; Gauss-Markov model; massive MIMO; WIRELESS; DOWNLINK;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this paper, the design of training signals for channel estimation in massive multiple-input multiple-output (MIMO) systems is considered. Under a stationary, block Gauss-Markov channel model, a method for optimal pilot beam pattern design for enhanced channel estimation is proposed, exploiting both the properties of Kalman filtering and the spatio-temporal channel correlation. First, pilot beam pattern design is considered under the assumption of orthogonal beam patterns within a block. The orthogonality assumption is subsequently relaxed and the design problem is solved via a greedy approach. Numerical results show the efficacy of the proposed algorithm.

引用

页数：5

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